Abstract

Genes do not act alone, rather they form part of large interacting networks
with certain genes regulating the activity of others. The structure of these networks
is of great importance as it can produce emergent behaviour, for instance, oscillations
in the expression of network genes or robustness to
uctuations. While some
networks have been studied in detail, most networks underpinning biological processes
have not been fully characterised. Elucidating the structure of these networks
is of paramount importance to understand these biological processes.
With the advent of whole-genome gene expression measurement technology,
a number of statistical methods have been put forward to predict the structure
of gene networks from the individual gene measurements. This thesis focuses on
the development of Bayesian statistical models for the inference of gene regulatory
networks using time-series data.
Most models used for network inference rely on the assumption that regulation
is linear. This assumption is known to be incorrect and when the interactions
are highly non-linear can affect the accuracy of the retrieved network. In order to
address this problem we developed an inference model that allows for non-linear
interactions and benchmarked the model against a linear interaction model.
Next we addressed the problem of how to infer a network when replicate
measurements are available. To analyse data with replicates we proposed two models
that account for measurement error. The models were compared to the standard
way of analysing replicate data, that is, calculating the mean/median of the data
and treating it as a noise-free time-series.
Following the development of the models we implemented GRENITS, an
R/Bioconductor package that integrates the models into a single free package. The
package is faster than the previous implementations and is also easier to use.
Finally GRENITS was used to fit a network to a whole-genome time-series
for the bacterium Streptomyces coelicolor. The accuracy of a sub-network of the inferred
network was assessed by comparing gene expression dynamics across datasets
collected under different experimental conditions.